An EEG-Based BCI System for Controlling Lower Exoskeleton to Step Over Obstacles in Realistic Walking Situation

The strategies to adopt brain-computer interfaces (BCIs) to drive assisted devices are proved to be feasible in many studies. Although several studies focus on detecting the initiation of normal walking by BCIs, few consider how to distinguish the change of gait pattern for different terrains in a realistic walking situation. Therefore, this paper proposes an innovative experimental paradigm for robust control of exoskeleton based on a BCI system. Several pseudo online trials are conducted to prove the feasibility of the proposed paradigm. Firstly, a labeled windows generator (LWG) is built to produce electroencephalogram (EEG) windows based on acquired gait data and EEG data. Then the common spatial pattern (CSP) is used to extract features from the labeled EEG windows. Finally, a support vector machine (SVM) classifier is trained to predict the intention of the subject. The experimental results corroborate the feasibility of obtaining the intention of stepping over obstacles from normal walking through the proposed BCI-controlled exoskeleton system.